Overview

Dataset statistics

Number of variables14
Number of observations5012
Missing cells814
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory548.3 KiB
Average record size in memory112.0 B

Variable types

Numeric6
Text7
Categorical1

Alerts

rank is highly overall correlated with growth_% and 1 other fieldsHigh correlation
growth_% is highly overall correlated with rank and 1 other fieldsHigh correlation
workers is highly overall correlated with previous_workersHigh correlation
previous_workers is highly overall correlated with workersHigh correlation
founded is highly overall correlated with rank and 1 other fieldsHigh correlation
metro has 813 (16.2%) missing valuesMissing
workers is highly skewed (γ1 = 43.76631282)Skewed
previous_workers is highly skewed (γ1 = 35.46097094)Skewed
founded is highly skewed (γ1 = -58.00396527)Skewed
rank is uniformly distributedUniform
profile has unique valuesUnique
name has unique valuesUnique
url has unique valuesUnique

Reproduction

Analysis started2023-08-25 20:57:09.286372
Analysis finished2023-08-25 20:57:15.856454
Duration6.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

rank
Real number (ℝ)

HIGH CORRELATION  UNIFORM 

Distinct4999
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2499.6283
Minimum1
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.3 KiB
2023-08-25T16:57:16.038490image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile250.55
Q11249.75
median2497.5
Q33749.25
95-th percentile4749.45
Maximum5000
Range4999
Interquartile range (IQR)2499.5

Descriptive statistics

Standard deviation1443.232
Coefficient of variation (CV)0.57737865
Kurtosis-1.1996566
Mean2499.6283
Median Absolute Deviation (MAD)1250
Skewness0.0008221728
Sum12528137
Variance2082918.6
MonotonicityIncreasing
2023-08-25T16:57:16.188596image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83 2
 
< 0.1%
2102 2
 
< 0.1%
4269 2
 
< 0.1%
4187 2
 
< 0.1%
1063 2
 
< 0.1%
2310 2
 
< 0.1%
903 2
 
< 0.1%
2433 2
 
< 0.1%
2011 2
 
< 0.1%
2417 2
 
< 0.1%
Other values (4989) 4992
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
5000 1
< 0.1%
4999 1
< 0.1%
4998 1
< 0.1%
4997 1
< 0.1%
4996 1
< 0.1%
4995 1
< 0.1%
4994 1
< 0.1%
4993 1
< 0.1%
4992 1
< 0.1%
4991 1
< 0.1%

profile
Text

UNIQUE 

Distinct5012
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
2023-08-25T16:57:16.440150image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length85
Median length73
Mean length44.269553
Min length29

Characters and Unicode

Total characters221879
Distinct characters62
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5012 ?
Unique (%)100.0%

Sample

1st rowhttps://www.inc.com/profile/freestar
2nd rowhttps://www.inc.com/profile/freightwise
3rd rowhttps://www.inc.com/profile/ceces-veggie
4th rowhttps://www.inc.com/profile/ladyboss
5th rowhttps://www.inc.com/profile/perpay
ValueCountFrequency (%)
https://www.inc.com/profile/freestar 1
 
< 0.1%
https://www.inc.com/profile/nom 1
 
< 0.1%
https://www.inc.com/profile/ladyboss 1
 
< 0.1%
https://www.inc.com/profile/perpay 1
 
< 0.1%
https://www.inc.com/profile/cano-health 1
 
< 0.1%
https://www.inc.com/profile/bear-mattress 1
 
< 0.1%
https://www.inc.com/profile/connected-solutions-group 1
 
< 0.1%
https://www.inc.com/profile/providence-healthcare-management 1
 
< 0.1%
https://www.inc.com/profile/homesnap 1
 
< 0.1%
https://www.inc.com/profile/urgently 1
 
< 0.1%
Other values (5002) 5002
99.8%
2023-08-25T16:57:16.872890image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 20048
 
9.0%
i 16028
 
7.2%
t 15769
 
7.1%
w 15745
 
7.1%
o 15712
 
7.1%
c 13776
 
6.2%
e 13223
 
6.0%
p 12288
 
5.5%
s 10662
 
4.8%
r 10558
 
4.8%
Other values (52) 78070
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 180481
81.3%
Other Punctuation 35086
 
15.8%
Dash Punctuation 5990
 
2.7%
Decimal Number 287
 
0.1%
Uppercase Letter 34
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 16028
 
8.9%
t 15769
 
8.7%
w 15745
 
8.7%
o 15712
 
8.7%
c 13776
 
7.6%
e 13223
 
7.3%
p 12288
 
6.8%
s 10662
 
5.9%
r 10558
 
5.8%
n 10521
 
5.8%
Other values (16) 46199
25.6%
Uppercase Letter
ValueCountFrequency (%)
I 3
 
8.8%
S 3
 
8.8%
D 3
 
8.8%
M 3
 
8.8%
C 3
 
8.8%
T 2
 
5.9%
B 2
 
5.9%
Q 2
 
5.9%
A 2
 
5.9%
G 2
 
5.9%
Other values (9) 9
26.5%
Decimal Number
ValueCountFrequency (%)
3 57
19.9%
2 56
19.5%
0 43
15.0%
1 39
13.6%
4 22
 
7.7%
6 19
 
6.6%
5 16
 
5.6%
8 12
 
4.2%
9 12
 
4.2%
7 11
 
3.8%
Other Punctuation
ValueCountFrequency (%)
/ 20048
57.1%
. 10024
28.6%
: 5012
 
14.3%
% 1
 
< 0.1%
@ 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 5990
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 180515
81.4%
Common 41364
 
18.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 16028
 
8.9%
t 15769
 
8.7%
w 15745
 
8.7%
o 15712
 
8.7%
c 13776
 
7.6%
e 13223
 
7.3%
p 12288
 
6.8%
s 10662
 
5.9%
r 10558
 
5.8%
n 10521
 
5.8%
Other values (35) 46233
25.6%
Common
ValueCountFrequency (%)
/ 20048
48.5%
. 10024
24.2%
- 5990
 
14.5%
: 5012
 
12.1%
3 57
 
0.1%
2 56
 
0.1%
0 43
 
0.1%
1 39
 
0.1%
4 22
 
0.1%
6 19
 
< 0.1%
Other values (7) 54
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 221879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 20048
 
9.0%
i 16028
 
7.2%
t 15769
 
7.1%
w 15745
 
7.1%
o 15712
 
7.1%
c 13776
 
6.2%
e 13223
 
6.0%
p 12288
 
5.5%
s 10662
 
4.8%
r 10558
 
4.8%
Other values (52) 78070
35.2%

name
Text

UNIQUE 

Distinct5012
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
2023-08-25T16:57:17.225796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length59
Median length44
Mean length16.339186
Min length1

Characters and Unicode

Total characters81892
Distinct characters79
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5012 ?
Unique (%)100.0%

Sample

1st rowFreestar
2nd rowFreightWise
3rd rowCece's Veggie Co.
4th rowLadyBoss
5th rowPerpay
ValueCountFrequency (%)
group 282
 
2.6%
solutions 254
 
2.3%
services 160
 
1.5%
137
 
1.2%
the 114
 
1.0%
technologies 102
 
0.9%
consulting 93
 
0.8%
systems 86
 
0.8%
partners 79
 
0.7%
technology 74
 
0.7%
Other values (5517) 9643
87.5%
2023-08-25T16:57:17.696846image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 7643
 
9.3%
6012
 
7.3%
i 5447
 
6.7%
o 5400
 
6.6%
n 5219
 
6.4%
a 5071
 
6.2%
r 4965
 
6.1%
t 4791
 
5.9%
s 3932
 
4.8%
l 3321
 
4.1%
Other values (69) 30091
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 62206
76.0%
Uppercase Letter 12890
 
15.7%
Space Separator 6012
 
7.3%
Other Punctuation 370
 
0.5%
Decimal Number 287
 
0.4%
Dash Punctuation 73
 
0.1%
Close Punctuation 20
 
< 0.1%
Open Punctuation 20
 
< 0.1%
Math Symbol 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7643
12.3%
i 5447
 
8.8%
o 5400
 
8.7%
n 5219
 
8.4%
a 5071
 
8.2%
r 4965
 
8.0%
t 4791
 
7.7%
s 3932
 
6.3%
l 3321
 
5.3%
c 2446
 
3.9%
Other values (16) 13971
22.5%
Uppercase Letter
ValueCountFrequency (%)
S 1714
13.3%
C 1293
 
10.0%
T 951
 
7.4%
P 871
 
6.8%
A 832
 
6.5%
M 817
 
6.3%
G 635
 
4.9%
B 605
 
4.7%
R 581
 
4.5%
E 558
 
4.3%
Other values (16) 4033
31.3%
Other Punctuation
ValueCountFrequency (%)
. 132
35.7%
& 130
35.1%
' 45
 
12.2%
, 32
 
8.6%
/ 19
 
5.1%
: 5
 
1.4%
@ 2
 
0.5%
" 2
 
0.5%
! 2
 
0.5%
% 1
 
0.3%
Decimal Number
ValueCountFrequency (%)
3 57
19.9%
2 56
19.5%
0 43
15.0%
1 39
13.6%
4 22
 
7.7%
6 19
 
6.6%
5 16
 
5.6%
8 12
 
4.2%
9 12
 
4.2%
7 11
 
3.8%
Math Symbol
ValueCountFrequency (%)
+ 10
71.4%
| 3
 
21.4%
~ 1
 
7.1%
Space Separator
ValueCountFrequency (%)
6012
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 75096
91.7%
Common 6796
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7643
 
10.2%
i 5447
 
7.3%
o 5400
 
7.2%
n 5219
 
6.9%
a 5071
 
6.8%
r 4965
 
6.6%
t 4791
 
6.4%
s 3932
 
5.2%
l 3321
 
4.4%
c 2446
 
3.3%
Other values (42) 26861
35.8%
Common
ValueCountFrequency (%)
6012
88.5%
. 132
 
1.9%
& 130
 
1.9%
- 73
 
1.1%
3 57
 
0.8%
2 56
 
0.8%
' 45
 
0.7%
0 43
 
0.6%
1 39
 
0.6%
, 32
 
0.5%
Other values (17) 177
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7643
 
9.3%
6012
 
7.3%
i 5447
 
6.7%
o 5400
 
6.6%
n 5219
 
6.4%
a 5071
 
6.2%
r 4965
 
6.1%
t 4791
 
5.9%
s 3932
 
4.8%
l 3321
 
4.1%
Other values (69) 30091
36.7%

url
Text

UNIQUE 

Distinct5012
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
2023-08-25T16:57:17.941720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length57
Median length37
Mean length16.664006
Min length6

Characters and Unicode

Total characters83520
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5012 ?
Unique (%)100.0%

Sample

1st rowhttp://freestar.com
2nd rowhttp://freightwisellc.com
3rd rowhttp://cecesveggieco.com
4th rowhttp://ladyboss.com
5th rowhttp://perpay.com
ValueCountFrequency (%)
http://freestar.com 1
 
< 0.1%
http://thisisnom.co 1
 
< 0.1%
http://ladyboss.com 1
 
< 0.1%
http://perpay.com 1
 
< 0.1%
http://canohealth.com 1
 
< 0.1%
http://bearmattress.com 1
 
< 0.1%
http://csgstore.net 1
 
< 0.1%
http://providencehcm.com 1
 
< 0.1%
homesnap.com 1
 
< 0.1%
geturgently.com 1
 
< 0.1%
Other values (5002) 5002
99.8%
2023-08-25T16:57:18.331642image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 8938
 
10.7%
c 7857
 
9.4%
e 6513
 
7.8%
m 6511
 
7.8%
t 5553
 
6.6%
. 5029
 
6.0%
a 4739
 
5.7%
i 4695
 
5.6%
s 4348
 
5.2%
r 4347
 
5.2%
Other values (32) 24990
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76291
91.3%
Other Punctuation 6659
 
8.0%
Decimal Number 339
 
0.4%
Dash Punctuation 231
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 8938
11.7%
c 7857
10.3%
e 6513
 
8.5%
m 6511
 
8.5%
t 5553
 
7.3%
a 4739
 
6.2%
i 4695
 
6.2%
s 4348
 
5.7%
r 4347
 
5.7%
n 4096
 
5.4%
Other values (16) 18694
24.5%
Decimal Number
ValueCountFrequency (%)
3 64
18.9%
2 63
18.6%
0 50
14.7%
1 46
13.6%
4 30
8.8%
6 23
 
6.8%
5 22
 
6.5%
8 15
 
4.4%
7 14
 
4.1%
9 12
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 5029
75.5%
/ 1098
 
16.5%
: 530
 
8.0%
, 1
 
< 0.1%
# 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 231
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76291
91.3%
Common 7229
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 8938
11.7%
c 7857
10.3%
e 6513
 
8.5%
m 6511
 
8.5%
t 5553
 
7.3%
a 4739
 
6.2%
i 4695
 
6.2%
s 4348
 
5.7%
r 4347
 
5.7%
n 4096
 
5.4%
Other values (16) 18694
24.5%
Common
ValueCountFrequency (%)
. 5029
69.6%
/ 1098
 
15.2%
: 530
 
7.3%
- 231
 
3.2%
3 64
 
0.9%
2 63
 
0.9%
0 50
 
0.7%
1 46
 
0.6%
4 30
 
0.4%
6 23
 
0.3%
Other values (6) 65
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 8938
 
10.7%
c 7857
 
9.4%
e 6513
 
7.8%
m 6511
 
7.8%
t 5553
 
6.6%
. 5029
 
6.0%
a 4739
 
5.7%
i 4695
 
5.6%
s 4348
 
5.2%
r 4347
 
5.2%
Other values (32) 24990
29.9%

state
Text

Distinct51
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
2023-08-25T16:57:18.539222image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10024
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZ
2nd rowTN
3rd rowTX
4th rowNM
5th rowPA
ValueCountFrequency (%)
ca 712
 
14.2%
tx 467
 
9.3%
fl 385
 
7.7%
ny 300
 
6.0%
va 288
 
5.7%
il 241
 
4.8%
ga 219
 
4.4%
pa 172
 
3.4%
oh 160
 
3.2%
co 156
 
3.1%
Other values (41) 1912
38.1%
2023-08-25T16:57:18.847816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1871
18.7%
C 1139
11.4%
N 888
 
8.9%
T 727
 
7.3%
L 727
 
7.3%
M 534
 
5.3%
I 496
 
4.9%
O 478
 
4.8%
X 467
 
4.7%
F 385
 
3.8%
Other values (14) 2312
23.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10024
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1871
18.7%
C 1139
11.4%
N 888
 
8.9%
T 727
 
7.3%
L 727
 
7.3%
M 534
 
5.3%
I 496
 
4.9%
O 478
 
4.8%
X 467
 
4.7%
F 385
 
3.8%
Other values (14) 2312
23.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 10024
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1871
18.7%
C 1139
11.4%
N 888
 
8.9%
T 727
 
7.3%
L 727
 
7.3%
M 534
 
5.3%
I 496
 
4.9%
O 478
 
4.8%
X 467
 
4.7%
F 385
 
3.8%
Other values (14) 2312
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1871
18.7%
C 1139
11.4%
N 888
 
8.9%
T 727
 
7.3%
L 727
 
7.3%
M 534
 
5.3%
I 496
 
4.9%
O 478
 
4.8%
X 467
 
4.7%
F 385
 
3.8%
Other values (14) 2312
23.1%

revenue
Text

Distinct1015
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
2023-08-25T16:57:19.227117image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.356544
Min length9

Characters and Unicode

Total characters56919
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique506 ?
Unique (%)10.1%

Sample

1st row36.9 Million
2nd row33.6 Million
3rd row24.9 Million
4th row32.4 Million
5th row22.5 Million
ValueCountFrequency (%)
million 4989
49.8%
2.2 65
 
0.6%
2.7 62
 
0.6%
2.3 61
 
0.6%
2.9 59
 
0.6%
2.1 57
 
0.6%
3 55
 
0.5%
2.5 54
 
0.5%
2 54
 
0.5%
2.6 52
 
0.5%
Other values (995) 4516
45.1%
2023-08-25T16:57:19.739187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 10024
17.6%
l 10024
17.6%
5012
8.8%
o 5012
8.8%
n 5012
8.8%
M 4989
8.8%
. 4469
7.9%
1 2068
 
3.6%
2 1909
 
3.4%
3 1515
 
2.7%
Other values (8) 6885
12.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30072
52.8%
Decimal Number 12354
21.7%
Space Separator 5012
 
8.8%
Uppercase Letter 5012
 
8.8%
Other Punctuation 4469
 
7.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2068
16.7%
2 1909
15.5%
3 1515
12.3%
4 1324
10.7%
5 1200
9.7%
6 1089
8.8%
7 1042
8.4%
8 925
7.5%
9 868
7.0%
0 414
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
i 10024
33.3%
l 10024
33.3%
o 5012
16.7%
n 5012
16.7%
Uppercase Letter
ValueCountFrequency (%)
M 4989
99.5%
B 23
 
0.5%
Space Separator
ValueCountFrequency (%)
5012
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4469
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35084
61.6%
Common 21835
38.4%

Most frequent character per script

Common
ValueCountFrequency (%)
5012
23.0%
. 4469
20.5%
1 2068
9.5%
2 1909
 
8.7%
3 1515
 
6.9%
4 1324
 
6.1%
5 1200
 
5.5%
6 1089
 
5.0%
7 1042
 
4.8%
8 925
 
4.2%
Other values (2) 1282
 
5.9%
Latin
ValueCountFrequency (%)
i 10024
28.6%
l 10024
28.6%
o 5012
14.3%
n 5012
14.3%
M 4989
14.2%
B 23
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 10024
17.6%
l 10024
17.6%
5012
8.8%
o 5012
8.8%
n 5012
8.8%
M 4989
8.8%
. 4469
7.9%
1 2068
 
3.6%
2 1909
 
3.4%
3 1515
 
2.7%
Other values (8) 6885
12.1%

growth_%
Real number (ℝ)

HIGH CORRELATION 

Distinct5006
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean454.68006
Minimum52.1691
Maximum36680.388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.3 KiB
2023-08-25T16:57:19.905093image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum52.1691
5-th percentile59.251195
Q190.5625
median157.53065
Q3330.42725
95-th percentile1701.2786
Maximum36680.388
Range36628.219
Interquartile range (IQR)239.86475

Descriptive statistics

Standard deviation1284.2887
Coefficient of variation (CV)2.8245987
Kurtosis249.22038
Mean454.68006
Median Absolute Deviation (MAD)83.24985
Skewness12.594038
Sum2278856.5
Variance1649397.5
MonotonicityDecreasing
2023-08-25T16:57:20.040994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.2981 2
 
< 0.1%
57 2
 
< 0.1%
102.7041 2
 
< 0.1%
89.1453 2
 
< 0.1%
164.2249 2
 
< 0.1%
59.4779 2
 
< 0.1%
107.6126 1
 
< 0.1%
107.4033 1
 
< 0.1%
107.5482 1
 
< 0.1%
107.5645 1
 
< 0.1%
Other values (4996) 4996
99.7%
ValueCountFrequency (%)
52.1691 1
< 0.1%
52.1919 1
< 0.1%
52.2037 1
< 0.1%
52.2127 1
< 0.1%
52.2377 1
< 0.1%
52.3467 1
< 0.1%
52.43 1
< 0.1%
52.4442 1
< 0.1%
52.4803 1
< 0.1%
52.5232 1
< 0.1%
ValueCountFrequency (%)
36680.3882 1
< 0.1%
30547.9317 1
< 0.1%
23880.4852 1
< 0.1%
21849.8925 1
< 0.1%
18166.407 1
< 0.1%
14183.4118 1
< 0.1%
13480.731 1
< 0.1%
12700.6588 1
< 0.1%
12564.5364 1
< 0.1%
11996.2964 1
< 0.1%

industry
Categorical

Distinct27
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
Business Products & Services
492 
Advertising & Marketing
489 
Software
461 
Health
356 
Construction
350 
Other values (22)
2864 

Length

Max length28
Median length21
Mean length16.216281
Min length5

Characters and Unicode

Total characters81276
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdvertising & Marketing
2nd rowLogistics & Transportation
3rd rowFood & Beverage
4th rowConsumer Products & Services
5th rowRetail

Common Values

ValueCountFrequency (%)
Business Products & Services 492
 
9.8%
Advertising & Marketing 489
 
9.8%
Software 461
 
9.2%
Health 356
 
7.1%
Construction 350
 
7.0%
Consumer Products & Services 315
 
6.3%
IT Management 276
 
5.5%
Financial Services 239
 
4.8%
Government Services 236
 
4.7%
Real Estate 198
 
4.0%
Other values (17) 1600
31.9%

Length

2023-08-25T16:57:20.179294image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1666
15.7%
services 1368
 
12.9%
products 807
 
7.6%
business 492
 
4.6%
advertising 489
 
4.6%
marketing 489
 
4.6%
software 461
 
4.3%
it 439
 
4.1%
health 356
 
3.4%
construction 350
 
3.3%
Other values (28) 3698
34.8%

Most occurring characters

ValueCountFrequency (%)
e 8841
 
10.9%
s 6201
 
7.6%
r 5844
 
7.2%
n 5816
 
7.2%
t 5757
 
7.1%
i 5717
 
7.0%
5603
 
6.9%
a 4399
 
5.4%
o 3968
 
4.9%
c 3653
 
4.5%
Other values (28) 25477
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64619
79.5%
Uppercase Letter 9388
 
11.6%
Space Separator 5603
 
6.9%
Other Punctuation 1666
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8841
13.7%
s 6201
9.6%
r 5844
9.0%
n 5816
9.0%
t 5757
8.9%
i 5717
8.8%
a 4399
 
6.8%
o 3968
 
6.1%
c 3653
 
5.7%
u 2958
 
4.6%
Other values (11) 11465
17.7%
Uppercase Letter
ValueCountFrequency (%)
S 2016
21.5%
M 992
10.6%
P 807
8.6%
T 761
 
8.1%
C 697
 
7.4%
B 619
 
6.6%
H 602
 
6.4%
R 518
 
5.5%
I 509
 
5.4%
A 489
 
5.2%
Other values (5) 1378
14.7%
Space Separator
ValueCountFrequency (%)
5603
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1666
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 74007
91.1%
Common 7269
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8841
11.9%
s 6201
 
8.4%
r 5844
 
7.9%
n 5816
 
7.9%
t 5757
 
7.8%
i 5717
 
7.7%
a 4399
 
5.9%
o 3968
 
5.4%
c 3653
 
4.9%
u 2958
 
4.0%
Other values (26) 20853
28.2%
Common
ValueCountFrequency (%)
5603
77.1%
& 1666
 
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8841
 
10.9%
s 6201
 
7.6%
r 5844
 
7.2%
n 5816
 
7.2%
t 5757
 
7.1%
i 5717
 
7.0%
5603
 
6.9%
a 4399
 
5.4%
o 3968
 
4.9%
c 3653
 
4.5%
Other values (28) 25477
31.3%

workers
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct635
Distinct (%)12.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean242.78308
Minimum0
Maximum155000
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size39.3 KiB
2023-08-25T16:57:20.309135image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q122
median48
Q3116
95-th percentile644.5
Maximum155000
Range155000
Interquartile range (IQR)94

Descriptive statistics

Standard deviation2800.1655
Coefficient of variation (CV)11.533611
Kurtosis2173.1322
Mean242.78308
Median Absolute Deviation (MAD)32
Skewness43.766313
Sum1216586
Variance7840926.7
MonotonicityNot monotonic
2023-08-25T16:57:20.456280image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 127
 
2.5%
15 101
 
2.0%
10 94
 
1.9%
30 94
 
1.9%
20 88
 
1.8%
50 86
 
1.7%
18 84
 
1.7%
12 81
 
1.6%
40 80
 
1.6%
17 72
 
1.4%
Other values (625) 4104
81.9%
ValueCountFrequency (%)
0 4
 
0.1%
1 11
 
0.2%
2 11
 
0.2%
3 18
 
0.4%
4 28
0.6%
5 46
0.9%
6 38
0.8%
7 56
1.1%
8 58
1.2%
9 52
1.0%
ValueCountFrequency (%)
155000 1
< 0.1%
96000 1
< 0.1%
58145 1
< 0.1%
22263 1
< 0.1%
20599 1
< 0.1%
14730 1
< 0.1%
14098 1
< 0.1%
13751 1
< 0.1%
10600 1
< 0.1%
10392 1
< 0.1%

previous_workers
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct443
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.68715
Minimum1
Maximum53000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.3 KiB
2023-08-25T16:57:20.608145image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median20
Q350
95-th percentile288.9
Maximum53000
Range52999
Interquartile range (IQR)42

Descriptive statistics

Standard deviation1073.8782
Coefficient of variation (CV)9.6150557
Kurtosis1504.069
Mean111.68715
Median Absolute Deviation (MAD)15
Skewness35.460971
Sum559776
Variance1153214.3
MonotonicityNot monotonic
2023-08-25T16:57:20.753300image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 219
 
4.4%
3 201
 
4.0%
15 200
 
4.0%
10 195
 
3.9%
2 187
 
3.7%
4 180
 
3.6%
8 178
 
3.6%
6 156
 
3.1%
12 143
 
2.9%
7 136
 
2.7%
Other values (433) 3217
64.2%
ValueCountFrequency (%)
1 112
2.2%
2 187
3.7%
3 201
4.0%
4 180
3.6%
5 219
4.4%
6 156
3.1%
7 136
2.7%
8 178
3.6%
9 104
2.1%
10 195
3.9%
ValueCountFrequency (%)
53000 1
< 0.1%
36469 1
< 0.1%
22263 1
< 0.1%
21000 1
< 0.1%
9833 1
< 0.1%
8958 1
< 0.1%
8100 1
< 0.1%
7257 1
< 0.1%
7036 1
< 0.1%
5500 1
< 0.1%

founded
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct83
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.4455
Minimum0
Maximum2016
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.3 KiB
2023-08-25T16:57:20.896653image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1987
Q12003
median2009
Q32012
95-th percentile2014
Maximum2016
Range2016
Interquartile range (IQR)9

Descriptive statistics

Standard deviation30.310632
Coefficient of variation (CV)0.015114164
Kurtosis3826.2687
Mean2005.4455
Median Absolute Deviation (MAD)4
Skewness-58.003965
Sum10051293
Variance918.73441
MonotonicityNot monotonic
2023-08-25T16:57:21.042006image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 466
 
9.3%
2012 440
 
8.8%
2013 431
 
8.6%
2011 377
 
7.5%
2009 363
 
7.2%
2010 360
 
7.2%
2008 299
 
6.0%
2007 255
 
5.1%
2006 198
 
4.0%
2004 188
 
3.8%
Other values (73) 1635
32.6%
ValueCountFrequency (%)
0 1
< 0.1%
1869 1
< 0.1%
1884 1
< 0.1%
1895 1
< 0.1%
1897 1
< 0.1%
1899 1
< 0.1%
1902 1
< 0.1%
1909 1
< 0.1%
1910 1
< 0.1%
1914 1
< 0.1%
ValueCountFrequency (%)
2016 1
 
< 0.1%
2015 172
 
3.4%
2014 466
9.3%
2013 431
8.6%
2012 440
8.8%
2011 377
7.5%
2010 360
7.2%
2009 363
7.2%
2008 299
6.0%
2007 255
5.1%

yrs_on_list
Real number (ℝ)

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8136472
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.3 KiB
2023-08-25T16:57:21.169521image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2659861
Coefficient of variation (CV)0.80535543
Kurtosis3.2676195
Mean2.8136472
Median Absolute Deviation (MAD)1
Skewness1.7418362
Sum14102
Variance5.134693
MonotonicityNot monotonic
2023-08-25T16:57:21.287931image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 1854
37.0%
2 1131
22.6%
3 659
 
13.1%
4 479
 
9.6%
5 293
 
5.8%
6 202
 
4.0%
7 132
 
2.6%
8 96
 
1.9%
9 61
 
1.2%
10 35
 
0.7%
Other values (4) 70
 
1.4%
ValueCountFrequency (%)
1 1854
37.0%
2 1131
22.6%
3 659
 
13.1%
4 479
 
9.6%
5 293
 
5.8%
6 202
 
4.0%
7 132
 
2.6%
8 96
 
1.9%
9 61
 
1.2%
10 35
 
0.7%
ValueCountFrequency (%)
14 5
 
0.1%
13 10
 
0.2%
12 20
 
0.4%
11 35
 
0.7%
10 35
 
0.7%
9 61
 
1.2%
8 96
 
1.9%
7 132
2.6%
6 202
4.0%
5 293
5.8%

metro
Text

MISSING 

Distinct70
Distinct (%)1.7%
Missing813
Missing (%)16.2%
Memory size39.3 KiB
2023-08-25T16:57:21.525981image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length36
Median length32
Mean length10.781138
Min length5

Characters and Unicode

Total characters45270
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhoenix
2nd rowNashville
3rd rowAustin
4th rowPhiladelphia
5th rowMiami
ValueCountFrequency (%)
city 461
 
6.5%
new 360
 
5.1%
york 353
 
5.0%
washington 321
 
4.5%
dc 321
 
4.5%
san 318
 
4.5%
los 306
 
4.3%
angeles 306
 
4.3%
chicago 237
 
3.3%
atlanta 199
 
2.8%
Other values (106) 3937
55.3%
2023-08-25T16:57:21.916964image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3790
 
8.4%
n 3255
 
7.2%
i 3113
 
6.9%
o 3091
 
6.8%
2920
 
6.5%
e 2832
 
6.3%
t 2404
 
5.3%
s 2367
 
5.2%
l 2273
 
5.0%
C 1490
 
3.3%
Other values (42) 17735
39.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31835
70.3%
Uppercase Letter 8863
 
19.6%
Space Separator 2920
 
6.5%
Other Punctuation 1254
 
2.8%
Dash Punctuation 398
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 1490
16.8%
A 924
10.4%
D 798
 
9.0%
L 671
 
7.6%
S 659
 
7.4%
N 629
 
7.1%
Y 404
 
4.6%
M 388
 
4.4%
O 356
 
4.0%
B 341
 
3.8%
Other values (14) 2203
24.9%
Lowercase Letter
ValueCountFrequency (%)
a 3790
11.9%
n 3255
10.2%
i 3113
9.8%
o 3091
9.7%
e 2832
8.9%
t 2404
 
7.6%
s 2367
 
7.4%
l 2273
 
7.1%
r 1309
 
4.1%
h 1276
 
4.0%
Other values (13) 6125
19.2%
Other Punctuation
ValueCountFrequency (%)
, 1182
94.3%
. 49
 
3.9%
/ 23
 
1.8%
Space Separator
ValueCountFrequency (%)
2920
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 398
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40698
89.9%
Common 4572
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3790
 
9.3%
n 3255
 
8.0%
i 3113
 
7.6%
o 3091
 
7.6%
e 2832
 
7.0%
t 2404
 
5.9%
s 2367
 
5.8%
l 2273
 
5.6%
C 1490
 
3.7%
r 1309
 
3.2%
Other values (37) 14774
36.3%
Common
ValueCountFrequency (%)
2920
63.9%
, 1182
25.9%
- 398
 
8.7%
. 49
 
1.1%
/ 23
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3790
 
8.4%
n 3255
 
7.2%
i 3113
 
6.9%
o 3091
 
6.8%
2920
 
6.5%
e 2832
 
6.3%
t 2404
 
5.3%
s 2367
 
5.2%
l 2273
 
5.0%
C 1490
 
3.3%
Other values (42) 17735
39.2%

city
Text

Distinct1558
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
2023-08-25T16:57:22.195837image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length21
Median length17
Mean length8.810854
Min length2

Characters and Unicode

Total characters44160
Distinct characters64
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique970 ?
Unique (%)19.4%

Sample

1st rowPhoenix
2nd rowBrentwood
3rd rowAustin
4th rowAlbuquerque
5th rowPhiladelphia
ValueCountFrequency (%)
san 189
 
2.9%
new 187
 
2.8%
york 172
 
2.6%
chicago 111
 
1.7%
city 105
 
1.6%
atlanta 101
 
1.5%
austin 87
 
1.3%
houston 84
 
1.3%
beach 83
 
1.3%
dallas 74
 
1.1%
Other values (1347) 5396
81.9%
2023-08-25T16:57:22.627571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4110
 
9.3%
e 3611
 
8.2%
n 3357
 
7.6%
o 3280
 
7.4%
l 2728
 
6.2%
i 2598
 
5.9%
r 2402
 
5.4%
t 2331
 
5.3%
s 1930
 
4.4%
1577
 
3.6%
Other values (54) 16236
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34816
78.8%
Uppercase Letter 7693
 
17.4%
Space Separator 1577
 
3.6%
Other Punctuation 62
 
0.1%
Decimal Number 10
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4110
11.8%
e 3611
10.4%
n 3357
9.6%
o 3280
9.4%
l 2728
 
7.8%
i 2598
 
7.5%
r 2402
 
6.9%
t 2331
 
6.7%
s 1930
 
5.5%
h 1042
 
3.0%
Other values (16) 7427
21.3%
Uppercase Letter
ValueCountFrequency (%)
S 733
 
9.5%
C 698
 
9.1%
A 629
 
8.2%
L 539
 
7.0%
B 481
 
6.3%
N 443
 
5.8%
M 413
 
5.4%
P 398
 
5.2%
R 396
 
5.1%
H 373
 
4.8%
Other values (16) 2590
33.7%
Decimal Number
ValueCountFrequency (%)
0 4
40.0%
9 2
20.0%
6 1
 
10.0%
3 1
 
10.0%
2 1
 
10.0%
1 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 45
72.6%
, 12
 
19.4%
' 4
 
6.5%
: 1
 
1.6%
Space Separator
ValueCountFrequency (%)
1577
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42509
96.3%
Common 1651
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4110
 
9.7%
e 3611
 
8.5%
n 3357
 
7.9%
o 3280
 
7.7%
l 2728
 
6.4%
i 2598
 
6.1%
r 2402
 
5.7%
t 2331
 
5.5%
s 1930
 
4.5%
h 1042
 
2.5%
Other values (42) 15120
35.6%
Common
ValueCountFrequency (%)
1577
95.5%
. 45
 
2.7%
, 12
 
0.7%
' 4
 
0.2%
0 4
 
0.2%
- 2
 
0.1%
9 2
 
0.1%
6 1
 
0.1%
3 1
 
0.1%
: 1
 
0.1%
Other values (2) 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4110
 
9.3%
e 3611
 
8.2%
n 3357
 
7.6%
o 3280
 
7.4%
l 2728
 
6.2%
i 2598
 
5.9%
r 2402
 
5.4%
t 2331
 
5.3%
s 1930
 
4.4%
1577
 
3.6%
Other values (54) 16236
36.8%

Interactions

2023-08-25T16:57:14.374137image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:10.367051image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:11.135762image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:11.915219image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:12.778266image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:13.532137image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:14.490511image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:10.523720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:11.260801image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:12.036002image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:12.892457image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:13.675843image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:14.669173image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:10.651978image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:11.399627image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:12.188160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:13.025190image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:13.806675image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:14.801996image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:10.777425image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:11.538504image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:12.327510image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:13.158583image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:13.949595image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:14.921797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:10.892546image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:11.655854image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:12.500230image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:13.276394image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:14.068873image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:15.056369image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:11.017279image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:11.789076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:12.644279image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:13.415350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-25T16:57:14.208028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-08-25T16:57:22.756952image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
rankgrowth_%workersprevious_workersfoundedyrs_on_listindustry
rank1.000-1.0000.1260.409-0.5300.3110.073
growth_%-1.0001.000-0.126-0.4090.530-0.3110.000
workers0.126-0.1261.0000.866-0.2840.3510.034
previous_workers0.409-0.4090.8661.000-0.4570.4500.031
founded-0.5300.530-0.284-0.4571.000-0.3460.000
yrs_on_list0.311-0.3110.3510.450-0.3461.0000.030
industry0.0730.0000.0340.0310.0000.0301.000

Missing values

2023-08-25T16:57:15.379567image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-25T16:57:15.622373image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-25T16:57:15.789443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

rankprofilenameurlstaterevenuegrowth_%industryworkersprevious_workersfoundedyrs_on_listmetrocity
01https://www.inc.com/profile/freestarFreestarhttp://freestar.comAZ36.9 Million36680.3882Advertising & Marketing40.0520151PhoenixPhoenix
12https://www.inc.com/profile/freightwiseFreightWisehttp://freightwisellc.comTN33.6 Million30547.9317Logistics & Transportation39.0820151NashvilleBrentwood
23https://www.inc.com/profile/ceces-veggieCece's Veggie Co.http://cecesveggieco.comTX24.9 Million23880.4852Food & Beverage190.01020151AustinAustin
34https://www.inc.com/profile/ladybossLadyBosshttp://ladyboss.comNM32.4 Million21849.8925Consumer Products & Services57.0220141NaNAlbuquerque
45https://www.inc.com/profile/perpayPerpayhttp://perpay.comPA22.5 Million18166.4070Retail25.0620141PhiladelphiaPhiladelphia
56https://www.inc.com/profile/cano-healthCano Healthhttp://canohealth.comFL271.8 Million14183.4118Health742.01820091MiamiMiami
67https://www.inc.com/profile/bear-mattressBear Mattresshttp://bearmattress.comNJ20.5 Million13480.7310Consumer Products & Services12.0120141New York CityHoboken
78https://www.inc.com/profile/connected-solutions-groupConnected Solutions Grouphttp://csgstore.netVA23.3 Million12700.6588Telecommunications72.0120151Richmond, VAMechanicsville
89https://www.inc.com/profile/providence-healthcare-managementProvidence Healthcare Managementhttp://providencehcm.comOH225.9 Million12564.5364Health60.01020081ClevelandCleveland
910https://www.inc.com/profile/nomNOMhttp://thisisnom.coCA21.4 Million11996.2964Advertising & Marketing37.0520141Los AngelesLos Angeles
rankprofilenameurlstaterevenuegrowth_%industryworkersprevious_workersfoundedyrs_on_listmetrocity
50024991https://www.inc.com/profile/DistilleryDistilleryhttp://distillery.comCA6.1 Million52.5232Software11.01320123Los AngelesSanta Monica
50034992https://www.inc.com/profile/american-thermal-systemsAmerican Thermal Systemsats-construction.comTX9.3 Million52.4803Construction19.01219981HoustonConroe
50044993https://www.inc.com/profile/red-six-mediaRed Six Mediaredsixmedia.comLA2 Million52.4442Advertising & Marketing16.0820091Baton Rouge, LABaton Rouge
50054994https://www.inc.com/profile/gemini-power-systemsGemini Power Systemsgeminipower.comFL93 Million52.4300Telecommunications35.02020001TampaSaint Petersburg
50064995https://www.inc.com/profile/golden-star-technologyGolden Star Technologygstes.comCA154.4 Million52.3467IT Services99.07019855Los AngelesCerritos
50074996https://www.inc.com/profile/village-plumbing-airVillage Plumbing & Airvillageplumbing.comTX15.8 Million52.2377Consumer Products & Services88.06219463HoustonHouston
50084997https://www.inc.com/profile/real-restoration-groupReal Restoration Grouprealrestoration.comIL11.6 Million52.2127Construction380.022020111ChicagoChicago
50094998https://www.inc.com/profile/naval-systemsNaval Systemsn-s-i.usMD29.7 Million52.2037Government Services187.012720041NaNLEXINGTON PARK
50104999https://www.inc.com/profile/hnm-systemsHNM Systemshnmsystems.comCA8.8 Million52.1919Telecommunications132.04720111San DiegoSolana Beach
50115000https://www.inc.com/profile/vivayicVivayicvivayic.comNE4.5 Million52.1691Business Products & Services27.02220064NaNLincoln